As automated assembly equipment and robots become more sophisticated, it is desirable to add the sense of vision to these devices. This would enable the equipment to locate the position of an object being manufactured, as well as to inspect the object for the presence of components and the proper location and size of specific features. To this end, various image processing systems have been devised which produce a two dimensional video signal image of the object for analysis.
A previous image processing technique transformed the original video signal image into a totally different one in which the desired features could be more readily analyzed. In preparing an image for transformation, a video signal image was initially digitized into a binary image, in which each picture element (pixel) is represented by a single bit so that the pixel is either black or white. Each pixel of the binary image is then transformed according to a definite rule specifying a new value for the particular pixel in terms of its old value and the old values of the pixels in a predefined neighborhood around it. For example, the neighborhood may consist of three-by-three matrix of pixels with the pixel being transformed located in the center of the matrix. The binary values of the nine neighborhood pixels are then evaluated to determine the new value for the central pixel. In a simple case, if all nine pixels are white, the central pixel is set to white in the transformed image; otherwise it is assigned a black level. The affect of this transformation is that the pixels at the edge of white areas become black. This case is referred to as "white erosion" since the white areas of the image become smaller and black areas become larger. An inverse transformation, referred to as "white dilation", exists where if certain neighborhood pixels are black, the transformed value of the central pixel is white. This later transformation tends to expand white areas of the image and decrease the black areas. This type of neighborhood transformation is generically known as "mathematical morphological processing" and is described in the book "Image Analysis and Mathematical Morphology" by Jean Serra published by Academic Press, Inc., 1982. Reference is also made to U.S. Pat. No. 3,805,035 entitled "Device for the Logical Analysis of Textures".
In using this morphological image processing technique, the transformation rule is often reapplied to the resultant image to further transform the original image. A number of these transformation iterations may be performed in order to resolve the image into one from which information regarding the relevant features can be more easily extracted. By the user defining the transformation rule and the number of processing iterations, a given feature in the image can be resolved into a unique image element, such as a point or a line in the resultant image.
As repetitive processing is often necessary, it is desirable to connect a number of morphological processors in a pipeline to speed up the transformation into the final version of the image. However, if the portions of the system which feed the image data into the pipeline or which receive the final resultant image enter a state in which they cannot interface with the pipeline, the integrity of the processing fails. Specifically, if the input of data to the pipeline ceases, continued pipeline operation will process invalid pixel data producing an invalid resultant image. Similarly, if the output portion of the system is unable to receive data from the pipeline, continued operation will result in processed image data being lost. These types of errors also may occur if an individual morphological processor in the pipeline encounters a fault condition and the other processors continue operating. Therefore, a mechanism is required to signal the processors when a fault condition exists and provide an orderly cessation of processing which maintains data integrity.